We applied resting-state functional magnetic resonance imaging (rfMRI) combined with graph theory to analyze 90 regions of the infantile small world neural network of the whole brain. We tried to get the following two points clear:① whether the parameters of the node property of the infantile small world neural network are correlated with the level of infantile intelligence development; ② whether the parameters of the infantile small world neural network are correlated with the children's baseline parameters, i.e., the demographic parameters such as gender, age, parents' education level, etc. Twelve cases of healthy infants were included in the investigation (9 males and 3 females with the average age of 33.42±8.42 months.) We then evaluated the level of infantile intelligence of all the cases and graded by Gesell Development Scale Test. We used a Siemens 3.0T Trio imaging system to perform resting-state (rs) EPI scans, and collected the BOLD functional Magnetic Resonance Imaging (fMRI) data. We performed the data processing with Statistical Parametric Mapping 5(SPM5) based on Matlab environment. Furthermore, we got the attributes of the whole brain small world and node attributes of 90 encephalic regions of templates of Anatomatic Automatic Labeling (ALL). At last, we carried out correlation study between the above-mentioned attitudes, intelligence scale parameters and demographic data. The results showed that many node attributes of small world neural network were closely correlated with intelligence scale parameters. Betweeness was mainly centered in thalamus, superior frontal gyrus, and occipital lobe (negative correlation). The r value of superior occipital gyrus associated with the individual and social intelligent scale was -0.729 (P=0.007); degree was mainly centered in amygdaloid nucleus, superior frontal gyrus, and inferior parietal gyrus (positive correlation). The r value of inferior parietal gyrus associated with the gross motor intelligent scale was 0.725 (P=0.008); efficiency was mainly centered in inferior frontal gyrus, inferior parietal gyrus, and insular lobe (positive correlation). The r value of inferior parietal gyrus associated with the language intelligent scale was 0.738 (P=0.006); Anoda cluster coefficient (anodalCp) was centered in frontal lobe, inferior parietal gyrus, and paracentral lobule (positive correlation); Node shortest path length (nlp) was centered in frontal lobe, inferior parietal gyrus, and insular lobe. The distribution of the encephalic regions in the left and right brain was different. However, no statistical significance was found between the correlation of monolithic attributes of small world and intelligence scale. The encephalic regions, in which node attributes of small world were related to other demographic indices, were mainly centered in temporal lobe, cuneus, cingulated gyrus, angular gyrus, and paracentral lobule areas. Most of them belong to the default mode network (DMN). The node attributes of small world neural network are widely related to infantile intelligence level, moreover the distribution is characteristic in different encephalic regions. The distribution of dominant encephalic is in accordance the related functions. The existing correlations reflect the ever changing small world nervous network during infantile development.
Mild cognitive impairment (MCI) is a clinical transition state between age-related cognitive decline and dementia. Researchers can use neuroimaging and neurophysiological techniques to obtain structural and functional information about the human brain. Using this information researchers can construct the brain network based on complex network theory. The literature on graph theory shows that the large-scale brain network of MCI patient exhibits small-world property, which ranges intermediately between Alzheimer's disease and that in the normal control group. But brain connectivity of MCI patients presents topologically structural disorder. The disorder is significantly correlated to the cognitive functions. This article reviews the recent findings on brain connectivity of MCI patients from the perspective of multimodal data. Specifically, the article focuses on the graph theory evidences of the whole brain structural and functional and the joint covariance network disorders. At last, the article shows the limitations and future research directions in this field.
The aim of this study is to explore the effects of continuous theta-burst transcranial magnetic stimulation (cTBS) on functional brain network in emotion processing. Before and after the intervention of cTBS over left dorsolateral prefrontal cortex (DLPFC) of ten participants who were asked to perform the emotion gender recognition task, we recorded their scalp electroencephalograms (EEG). Then we used the phase synchronization of EEG to measure the connectivity between two nodes. We then calculated the network efficiency to describe the efficiency of information transmission in brain regions. Our research showed that after the intervention of cTBS and the stimulation of the emotion face picture, there was an obvious enhancement in the event-related spectral perturbation after stimuli onset in beta band in 100–300 ms. Under the stimulation of different emotion picture, the values of global phase synchronization for negative and neutral stimuli were enhanced compared to positive ones. And the increased small-worldness was found in emotional processing. In summary, based on the effect of activity change in the left DLPFC on emotion processing brain network, the emotional processing mechanism of brain networks were preliminary explored and it provided the reference for the research of emotion processing brain network in the future.
Real-world data (RWD) in clinical research on specific categories of medical devices can generate sufficient quality evidence which will be used in decision making. This paper discusses the limitations of traditional randomized controlled trials in clinical research of medical devices, summarizes and analyses the applicable conditions of real-world evidence (RWE) for medical devices, interprets the new FDA guidance document on the characteristics of RWD for medical devices, in order to provide evidence for the use of RWE in medical devices in our country.
Compared with traditional clinical trials, the real-world studies set higher requirements on the authenticity (reality), applicability, and timeliness of the evidence obtained. In this paper, we brought up a hypothesis that creating synergies between observational and experimental studies may meet these requirements. And then it was discussed in three aspects including providing evidence, research design and execution. In addition, data analysis facilitated generating efficient and robust evidence which was in support of decision making. Finally, some enlightenment may be offered for Traditional Chinese Medicine evaluation methods based on the synergies of both study types.
Randomized double-blind controlled trials (RCTs) conduct researches in carefully selected populations to ensure results of RCTs are unaffected by external disturbances and provide evidence of safety and efficacy. Real-world researches further help to understand the real world effects of new technologies in different medical environments after-market authorization. RCTs are the evidence foundation of real-world researches, and real-world researches provide valuable complement to RCTs. Medical insurance database is one of the most important database in real-world researches. Now, China's national medical insurance is entering a new era and transits from passive payment and compensation into a value-based strategic purchase mechanism for its insured population to buy the most cost-effective services. It is necessary to establish a mature, well-organized and value-based mechanism. The core of such mechanism is values, which is the price/performance ratio of innovative medicines and technologies rather than looking at the price solely. Demonstrating innovative drug value is an essential part of health care assessment. The authors argue that the assessment of the overall value of innovative technologies or medicines should include and based on the following four dimensions: clinical value, economic value, patient value and society value.
Real-world evidence represents critical evidence to support post-marketing drug monitoring, assessment and policy decisions, and has received extensive attentions. However, an explicit over-arching design and conceptual framework for this specific area is lacking. Divergent opinions on the production of real world evidence are often present among researchers; and understanding about their implications also differ among policy makers and evidence users. In this article, we have proposed, from the regulatory and clinical perspectives, a conceptual framework on the use of real world data for post-marketing drug studies, assessment and policy decisions.
The artificial neural network has the ability of the information processing and storage, good adaptability, strong learning function, association function and fault tolerance function. The research on the artificial neural network is mostly focused on the dynamic properties due to fact that the applications of artificial neural networks are related to its dynamic properties. At present, the researches on the neural network are based on the hierarchical network which can not simulate the real neural network. As a high level of abstraction of real complex systems, the small world network has the properties of biological neural networks. In the study, the small world network was constructed and the optimal parameter of the small word network was chosen based on the complex network theory firstly. And then based on the regulation mechanism of the synaptic plasticity and the topology of the small world network, the small world neural network was constructed and dynamic properties of the neural network were analyzed from the three aspects of the firing properties, dynamic properties of synaptic weights and complex network properties. The experimental results showed that with the increase of the time, the firing patterns of excitatory and inhibitory neurons in the small world neural network didn’t change and the firing time of the neurons tended to synchronize; the synaptic weights between the neurons decreased sharply and eventually tended to be steady; the connections in the neural network were weakened and the efficiency of the information transmission was reduced, but the small world attribute was stable. The dynamic properties of the small world neural network vary with time, and the dynamic properties can also interact with each other: the firing synchronization of the neural network can affect the distribution of synaptic weights to the minimum, and then the dynamic changes of the synaptic weights can affect the complex network properties of the small world neural network.
Recently, real world studies (RWS) have received increasing attentions. Such studies typically involve patient information, and their results may have potentially significant impact on patient well-being and safety. When reviewing the protocol of real world studies, ethical issues should be carefully considered and assessed. This paper discussed three issues, including the overview of bioethics and its application to classic clinical trials, key features of RWS, and medical ethical considerations on RWS.
Objectives To analyze the labeling of drug items for patients with renal insufficiency in our hospital so as to provide reference for rational use of drugs. Methods The drug instructions used in No.1 Hospital of Jilin University in 2017 were collected. According to the classification of pharmacology, the instructions of the top 9 drugs system were selected. The annotation of drug items for patients with renal insufficiency in these pharmaceutical instructions was analyzed. Results A total of 812 drug instructions were included, in which 72.17% did not mark drug instruction for renal insufficiency patients. According to the administration system, the highest unlabeled rate were digestive system drugs; according to the method of administration, the highest unlabeled rate was external preparation of drugs, accounting for 83.33%; according to production enterprises, the highest unlabeled rate were domestic drugs, accounting for 75.55%. There were only 23.40% of the tagging items having guidance of medication, and some with a certain degree of confusion in the annotation. Conclusion The unlabelling situation of drug instructions for renal insufficiency patients is very serious. It should arouse the attention of pharmaceutical manufacturers and the pharmaceutical supervisory department is suggested to strengthen the supervision of drug instructions and regulate the contents of drug labeling in drug instructions, so as to guide the rational use of drugs in clinical practice.